Integrating and guiding collaboration

Journal of Applied Instructional Design, 2013

Research has shown that learners have difficulty as they transition to ill-structured problems tasks. One way for educators to facilitate this transition is by applying case-based reasoning (CBR) theory in the form of case library learning environments. However, the research about how to best implement CBR theory remains limited. This is surprising given the importance of cases within goal-based scenarios, anchored instruction, problem-based learning, and cognitive flexibility theory. As such, further research of this educational technology will help ensure the application of these learning environments establish a foundation for effective higher order learning and problem-solving expertise. The current study evaluated three conditions (lecture, case library used in a collaborative context; case library used on an individual basis) on a problem-solving task. Results found the CBR collaboration group outperformed the other conditions on a posttest designed for problem-solving. Implications for implementation for instructional design are also discussed.

This paper provides a conceptual overview of how artificial intelligence can be used in learning process. Case-based Reasoning (CBR) is part of artificial intelligence that provides a model of learning problem-solving. Problemsolving in the Case-based Reasoning is done by reusing previous solutions that have similarity. Thedevelopment of Case-based Reasoning is strongly influenced by cognitive science, many studies have proven the success of the Case-based Reasoning in learning. Case-based Reasoning is able to be an alternative solution to develop problem-solving skills of students in learning.

1996

Abstract The modern education community agrees that deep and effective learning is best promoted by situating learning in authentic activity. Many in the education community have put in place constructivist classroom practices that put students into situations where they must make hypotheses, collect data, and determine which data to use in the process of solving a problem or participating in some kind of realistic analysis or investigation.

2002

is a kind of analogical reasoning that focuses on reasoning based on previous experience. A previous experience can play several roles: • suggest a solution to a new problem or a way of interpreting a situation, • warn of a problem that will arise, or • allow the potential effects of a proposed solution to be predicted. These are the kinds of inference necessary for addressing the kinds of ill-defined or complex problems that come our way everyday in the workplace, at school, and at home. We might, for example, create a new recipe by adapting one we've made previously. To understand why someone's boss reacted a certain way, we might remember a situation when our own boss reacted similarly. We might persuade ourselves that a strategic plan will work based on the similarities between our company's situation and those of another company that is progressing in a similar way. CBR views analogical reasoning as the centerpiece of our ability to function as human beings. It posits that our most natural and powerful learning strategies are the automatic ones that situate learning in real-world experience. We naturally bring our previous experience and knowledge to bear in interpreting new situations we encounter, try to explain when things aren't as expected (based on the predictions made by our previous experiences and knowledge), draw conclusions based on explanations and on similarities between situations, and anticipate when this new thing we've just learned might be applicable. To do these things automatically, we must have some internal processes and representations that allow a new experience to call up similar ones from memory. Key to such reasoning is a memory that can access the right experiences (cases) at the times they are needed (the indexing problem). CBR identifies two sets of procedures that allow such recognition to happen. (1) At insertion (encoding) time, while engaging in an experience, a reasoner interprets the situation and identifies at least some of the lessons it can teach and when those lessons might most productively be applied. The case is labeled according to its applicability conditions, i.e., the circumstances in which it ought to be retrieved. The most discriminating labels on a case will be derived by a reasoner that has taken the time and effort, and that has the background knowledge, to carefully analyze a case's potential applicability. (2)

The Knowledge Engineering Review, 2005

This commentary briefly reviews work on the application of case-based reasoning (CBR) to the design and construction of educational approaches and computer-based teaching systems. The CBR cognitive model is at the core of constructivist learning approaches such as Goal-Based Scenarios and Learning by Design. Case libraries can play roles as intelligent resources while learning and frameworks for articulating one's understanding. More recently, CBR techniques have been applied to design and construction of simulation-based learning systems and serious games. The main ideas of CBR are explained and pointers to relevant references are provided, both for finished work and on-going research..